Predictive tree-based geometry coding for a point cloud

Abstract

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on reducing a size of an array corresponding to syntax elements for predictive tree-based coding of the point cloud. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

What is claimed is: 
     
       1. A method of encoding point cloud data, executable by a processor, comprising:
 receiving data corresponding to a point cloud; 
 determining a number of contexts associated with the received data based on reducing a size of a context array corresponding to syntax elements for predictive tree-based coding of the point cloud,
 wherein the context array is a three-dimensional array that indicates a total number of contexts required to decode the syntax elements associated with the received data, 
 wherein the reducing comprises reducing a size of at least one dimension of the context array from a first value to a second value less than the first value; and 
 
 encoding the received data corresponding to the point cloud based on the reduced number of contexts. 
 
     
     
       2. The method of  claim 1 , wherein the size of the context array is reduced based on reducing a number of possible bit values for the contexts. 
     
     
       3. The method of  claim 1 , wherein a current node associated with the point cloud includes three geometry position residual components. 
     
     
       4. The method of  claim 3 , wherein the size of the context array is reduced based on the three geometry position residual components sharing the same contexts. 
     
     
       5. The method of  claim 1 , wherein the size of the context array is reduced based on reducing a number of possible indices for the contexts. 
     
     
       6. The method of  claim 1 , wherein the received data is encoded based on using a predictive tree to encode a largest coding unit. 
     
     
       7. The method of  claim 6 , further comprising encoding a number of points in the largest coding unit through predictive-tree based coding based on treating the largest coding unit as a smaller point cloud. 
     
     
       8. A computer system for decoding point cloud data, the computer system comprising:
 one or more computer-readable non-transitory storage media configured to store computer program code; and 
 one or more computer processors configured to access said computer program code and operate as instructed by said computer program code, said computer program code including:
 receiving code configured to cause the one or more computer processors to receive data corresponding to a point cloud; 
 determining code configured to cause the one or more computer processors to reduce a number of contexts associated with the received data based on reducing a size of an context array corresponding to syntax elements for predictive tree-based coding of the point cloud,
 wherein the context array is a three-dimensional array that indicates a total number of contexts required to decode the syntax elements associated with the received data, 
 wherein the reducing comprises reducing a size of at least one dimension of the context array from a first value to a second value less than the first value; and 
 
 first encoding code configured to cause the one or more computer processors to decode the received data corresponding to the point cloud based on the reduced number of contexts. 
 
 
     
     
       9. The computer system of  claim 8 , wherein the size of the context array is reduced based on reducing a number of possible bit values for the contexts. 
     
     
       10. The computer system of  claim 8 , wherein a current node associated with the point cloud includes three geometry position residual components. 
     
     
       11. The computer system of  claim 10 , wherein the size of the context array is reduced based on the three geometry position residual components sharing the same contexts. 
     
     
       12. The computer system of  claim 8 , wherein the size of the context array is reduced based on reducing a number of possible indices for the contexts. 
     
     
       13. The computer system of  claim 8 , wherein the received data is encoded based on using a predictive tree to decode a largest coding unit associated with the point cloud. 
     
     
       14. The computer system of  claim 13 , further comprising second encoding code configured to cause the one or more computer processors to encode a number of points in the largest coding unit through predictive-tree based coding based on treating the largest coding unit as a smaller point cloud. 
     
     
       15. A non-transitory computer readable medium having stored thereon a computer program for encoding point cloud data, the computer program configured to cause one or more computer processors to:
 receive data corresponding to a point cloud; 
 determine a number of contexts associated with the received data based on reducing a size of an context array corresponding to syntax elements for predictive tree-based coding of the point cloud,
 wherein the context array is a three-dimensional array that indicates a total number of contexts required to decode the syntax elements associated with the received data, 
 wherein the reducing comprises reducing a size of at least one dimension of the context array from a first value to a second value less than the first value; and 
 
 encode the received data corresponding to the point cloud based on the reduced number of contexts. 
 
     
     
       16. The computer readable medium of  claim 15 , wherein the size of the context array is reduced based on reducing a number of possible bit values for the contexts. 
     
     
       17. The computer readable medium of  claim 15 , wherein a current node associated with the point cloud includes three geometry position residual components. 
     
     
       18. The computer readable medium of  claim 17 , wherein the size of the context array is reduced based on the three geometry position residual components sharing the same contexts. 
     
     
       19. The computer readable medium of  claim 15 , wherein the size of the context array is reduced based on reducing a number of possible indices for the contexts. 
     
     
       20. The computer readable medium of  claim 15 , wherein the received data is encoded based on using a predictive tree to decode a largest coding unit associated with the point cloud.